ReFr is a software architecture for specifying, training and using reranking models. Reranking models take the n-best output of some existing system and produce new scores for each of the n hypotheses that potentially induce a different ranking, ideally yielding better results than the original system. The Reranker Framework (ReFr for short) has some special support for building discriminative language models, but can be applied to any reranking problem.